Machine Learning Based Test Coverage In A Production Environment

1. A computing platform, comprising: at least one processor; and memory storing computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: retrieve, by a computing device and for a software application, first log data associated with user navigation activity in one or more webpages in a production environment; generate, by the computing device and based on the user navigation activity, a production navigational graph for the software application, wherein a node of the production navigational graph represents a webpage visited by a user, and an edge between two nodes represents a navigational link between two webpages visited by the user; retrieve, by the computing device and for the software application, second log data associated with testing activity in a testing environment; generate, by the computing device and based on the testing activity, a testing navigational graph for the software application, wherein a node of the testing navigational graph represents a webpage tested by a developer, and an edge between two nodes represents a navigational link between two webpages, wherein the navigational link has been tested by the developer; generate, using a machine learning model, based on the production navigational graph and the testing navigational graph, a coverage graph indicative of a gap between the user navigation activity and the testing activity; and provide, via an interactive graphical user interface, the coverage graph, wherein nodes and edges of the coverage graph are associated with selectable visual objects.

2. The computing platform of claim 1 , wherein the instructions comprise additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: detect, in the coverage graph, a node visited by the user in the production environment and not tested by the developer in the testing environment; and display, by the computing device, the detected node with a first color.

3. The computing platform of claim 2 , wherein the instructions comprise additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: determine that the detected node has been subsequently tested by the developer; and display, by the computing device, the detected node with a second color different from the first color.

4. The computing platform of claim 1 , wherein the instructions comprise additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: detect, in the coverage graph, a link visited by the user in the production environment, wherein the link is associated with an error in the production environment; and display, by the computing device, the detected link with a first color indicating the error.

5. The computing platform of claim 4 , wherein the instructions comprise additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: determine that the error associated with the detected link has been subsequently corrected by the developer; and display, by the computing device, the detected link with a second color different from the first color.

6. The computing platform of claim 1 , wherein the instructions comprise additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: receive, via the interactive graphical user interface, an indication of a selection of a selectable visual object associated with a link; and display a message from a service call associated with the link.

7. The computing platform of claim 1 , wherein the instructions comprise additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: receive, via the interactive graphical user interface, an indication of a selection of a selectable visual object associated with a link; and display, for a user and a session and via the interactive graphical user interface, a session activity graph indicating user activity during the session.

8. The computing platform of claim 1 , wherein the instructions comprise additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: receive, via the interactive graphical user interface, an indication of a selection of a selectable visual object associated with a node; and display, via the interactive graphical user interface, a portion of a code associated with the node.

9. The computing platform of claim 1 , wherein the instructions comprise additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: display, via the interactive graphical user interface, a coverage score indicative of the gap between the user navigation activity in the production environment and the testing activity in the testing environment.

10. The computing platform of claim 1 , wherein the instructions comprise additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: associate, with a given link of a plurality of links between the one or more webpages and based on a pattern of traffic for the plurality of links, a first link score indicative of a number of times the given link is traversed in the production environment; associate, with the given link, a second link score indicative of a number of times the given link has an error in the production environment; and execute an error correction strategy based on the first link score and the second link score.

11. The computing platform of claim 1 , wherein the instructions comprise additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: train the machine learning model to determine: a first link score indicative of a number of times a given link is traversed in the production environment, and a second link score indicative of a number of times the given link is associated with an error in the production environment; and execute an error correction strategy based on the first link score and the second link score.

12. The computing platform of claim 1 , wherein the instructions comprise additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: associate, with a given webpage of the one or more webpages and based on a pattern of traffic for the one or more webpages, a first webpage score indicative of a number of times the given webpage is visited in the production environment; associate, with the given webpage, a second webpage score indicative of a number of times the given webpage is associated with an error in the production environment; and execute an error correction strategy based on the first webpage score and the second webpage score.

13. The computing platform of claim 1 , wherein the instructions comprise additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: train the machine learning model to determine: a first webpage score indicative of a number of times the given webpage is visited in the production environment, and a second webpage score indicative of a number of times the given webpage has an error in the production environment; and execute an error correction strategy based on the first webpage score and the second webpage score.

14. The computing platform of claim 1 , wherein the instructions comprise additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: detect, by the computing device and based on the gap, one or more of a webpage or a link that was not tested in the testing environment.

15. The computing platform of claim 1 , wherein the instructions comprise additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: detect, by the computing device and based on the gap, a link that was not identified in the testing environment.

16. The computing platform of claim 1 , wherein the instructions comprise additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: update, based on the first log data, the production navigational graph; update, based on the second log data, the testing navigational graph; and update, based on the updated production navigational graph and the updated testing navigational graph, the coverage graph.

17. The computing platform of claim 1 , wherein the production environment and the testing environment are based on one or more of: a type of service, a type of user, and a type of computing device.

18. A method, comprising: at a computing platform comprising at least one processor, and memory: retrieving, by a computing device and for a software application, first log data associated with user navigation activity in one or more webpages in a production environment; generating, by the computing device and based on the user navigation activity, a production navigational graph for the software application, wherein a node of the production navigational graph represents a webpage visited by a user, and an edge between two nodes represents a navigational link between two webpages visited by the user; retrieving, by the computing device and for the software application, second log data associated with testing activity in a testing environment; generating, by the computing device and based on the testing activity, a testing navigational graph for the software application, wherein a node of the testing navigational graph represents a webpage tested by a developer, and an edge between two nodes represents a navigational link between two webpages, wherein the navigational link has been tested by the developer; analyzing, based on the production navigational graph and the testing navigational graph, a gap between the user navigation activity and the testing activity; generating, using a machine learning model, based on analyzing, a coverage graph indicative of the gap; and providing, via an interactive graphical user interface, the coverage graph, wherein nodes and edges of the coverage graph are associated with selectable visual objects.

19. The method of claim 18 , further comprising: detecting, by the computing device and based on the gap, one or more of a webpage or a link that was not tested in the testing environment.

20. One or more non-transitory computer-readable media storing instructions that, when executed by a computing platform comprising at least one processor, and memory, cause the computing platform to: retrieve, in real-time and by a computing device and for a software application, first log data associated with user navigation activity in one or more webpages in a production environment; generate, by the computing device and based on the user navigation activity, a production navigational graph for the software application, wherein a node of the production navigational graph represents a webpage visited by a user, and an edge between two nodes represents a navigational link between two webpages visited by the user; retrieve, by the computing device and for the software application, second log data associated with testing activity in a testing environment; generate, by the computing device and based on the testing activity, a testing navigational graph for the software application, wherein a node of the testing navigational graph represents a webpage tested by a developer, and an edge between two nodes represents a navigational link between two webpages, wherein the navigational link has been tested by the developer; generate, using a machine learning model, based on the production navigational graph and the testing navigational graph, a coverage graph indicative of a gap between the user navigation activity and the testing activity; and provide, via an interactive graphical user interface, the coverage graph, wherein nodes and edges of the coverage graph are associated with selectable visual objects.